Initial and periodic validation of quant models
Quantitative analysis and review of model frameworks, assumptions, data, and results
Designing, modelling and prototyping challenger models when required
Testing models numerical implementations and reviewing documentations
Checking the adherence to governance requirements
Documentation of findings invalidation reports, including raising recommendations for model improvements
Ensuring models are validated in line with regulatory requirements and industry best practice
Tracking remediation of validation recommendations
Preparation of model risk reporting for Model Oversight Committee and Board
Ph.D. (Preferred) or MSc in a quantitative subject (Mathematics, Statistics, Applied Mathematics, Mathematical Finance, Physics, etc)
Experience in risk-modelling of trading book or banking book portfolios (model development or validation)
Experience in market risk or/and counterparty risk modelling
Experience with other risk models (Economic Capital, Stress Testing, etc.)
Strong background in Math and Probability theory - applied to finance.
Good understanding of financial products.
Good programming level in Python or R or equivalent.
Awareness of the latest technical developments in financial mathematics, pricing, and risk modelling
Up-to-date knowledge of regulatory capital requirements for market and credit risk
Experience with derivatives pricing models
Good knowledge of Data Science and Statistical inference techniques.
Modelling and pricing of financial derivatives
Computer simulations and numerical approximation methods
Experience with C++ or C# or equivalent